Delete config.py

config.py

@@ -1,189 +0,0 @@
-norm_cfg = dict(type='SyncBN', requires_grad=True)
-model = dict(
-    type='EncoderDecoder',
-    pretrained='pretrained/swin_small_patch4_window7_224.pth',
-    backbone=dict(
-        type='SwinTransformer',
-        embed_dim=96,
-        depths=[2, 2, 18, 2],
-        num_heads=[3, 6, 12, 24],
-        window_size=7,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        qk_scale=None,
-        drop_rate=0.0,
-        attn_drop_rate=0.0,
-        drop_path_rate=0.3,
-        ape=False,
-        patch_norm=True,
-        out_indices=(0, 1, 2, 3),
-        use_checkpoint=False),
-    decode_head=dict(
-        type='UPerHead',
-        in_channels=[96, 192, 384, 768],
-        in_index=[0, 1, 2, 3],
-        pool_scales=(1, 2, 3, 6),
-        channels=512,
-        dropout_ratio=0.1,
-        num_classes=104,
-        norm_cfg=dict(type='SyncBN', requires_grad=True),
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
-    auxiliary_head=dict(
-        type='FCNHead',
-        in_channels=384,
-        in_index=2,
-        channels=256,
-        num_convs=1,
-        concat_input=False,
-        dropout_ratio=0.1,
-        num_classes=104,
-        norm_cfg=dict(type='SyncBN', requires_grad=True),
-        align_corners=False,
-        loss_decode=dict(
-            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
-    train_cfg=dict(),
-    test_cfg=dict(mode='whole'))
-dataset_type = 'CustomDataset'
-data_root = './data/FoodSeg103/Images/'
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
-crop_size = (512, 1024)
-train_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(type='LoadAnnotations'),
-    dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
-    dict(type='RandomCrop', crop_size=(512, 1024), cat_max_ratio=0.75),
-    dict(type='RandomFlip', prob=0.5),
-    dict(type='PhotoMetricDistortion'),
-    dict(
-        type='Normalize',
-        mean=[123.675, 116.28, 103.53],
-        std=[58.395, 57.12, 57.375],
-        to_rgb=True),
-    dict(type='Pad', size=(512, 1024), pad_val=0, seg_pad_val=255),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_semantic_seg'])
-]
-test_pipeline = [
-    dict(type='LoadImageFromFile'),
-    dict(
-        type='MultiScaleFlipAug',
-        img_scale=(2048, 1024),
-        flip=False,
-        transforms=[
-            dict(type='Resize', keep_ratio=True),
-            dict(type='RandomFlip'),
-            dict(
-                type='Normalize',
-                mean=[123.675, 116.28, 103.53],
-                std=[58.395, 57.12, 57.375],
-                to_rgb=True),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img'])
-        ])
-]
-data = dict(
-    samples_per_gpu=2,
-    workers_per_gpu=2,
-    train=dict(
-        type='CustomDataset',
-        data_root='./data/FoodSeg103/Images/',
-        img_dir='img_dir/train',
-        ann_dir='ann_dir/train',
-        pipeline=[
-            dict(type='LoadImageFromFile'),
-            dict(type='LoadAnnotations'),
-            dict(
-                type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
-            dict(type='RandomCrop', crop_size=(512, 1024), cat_max_ratio=0.75),
-            dict(type='RandomFlip', prob=0.5),
-            dict(type='PhotoMetricDistortion'),
-            dict(
-                type='Normalize',
-                mean=[123.675, 116.28, 103.53],
-                std=[58.395, 57.12, 57.375],
-                to_rgb=True),
-            dict(type='Pad', size=(512, 1024), pad_val=0, seg_pad_val=255),
-            dict(type='DefaultFormatBundle'),
-            dict(type='Collect', keys=['img', 'gt_semantic_seg'])
-        ]),
-    val=dict(
-        type='CustomDataset',
-        data_root='./data/FoodSeg103/Images/',
-        img_dir='img_dir/test',
-        ann_dir='ann_dir/test',
-        pipeline=[
-            dict(type='LoadImageFromFile'),
-            dict(
-                type='MultiScaleFlipAug',
-                img_scale=(2048, 1024),
-                flip=False,
-                transforms=[
-                    dict(type='Resize', keep_ratio=True),
-                    dict(type='RandomFlip'),
-                    dict(
-                        type='Normalize',
-                        mean=[123.675, 116.28, 103.53],
-                        std=[58.395, 57.12, 57.375],
-                        to_rgb=True),
-                    dict(type='ImageToTensor', keys=['img']),
-                    dict(type='Collect', keys=['img'])
-                ])
-        ]),
-    test=dict(
-        type='CustomDataset',
-        data_root='./data/FoodSeg103/Images/',
-        img_dir='img_dir/test',
-        ann_dir='ann_dir/test',
-        pipeline=[
-            dict(type='LoadImageFromFile'),
-            dict(
-                type='MultiScaleFlipAug',
-                img_scale=(2048, 1024),
-                flip=False,
-                transforms=[
-                    dict(type='Resize', keep_ratio=True),
-                    dict(type='RandomFlip'),
-                    dict(
-                        type='Normalize',
-                        mean=[123.675, 116.28, 103.53],
-                        std=[58.395, 57.12, 57.375],
-                        to_rgb=True),
-                    dict(type='ImageToTensor', keys=['img']),
-                    dict(type='Collect', keys=['img'])
-                ])
-        ]))
-log_config = dict(
-    interval=50, hooks=[dict(type='TextLoggerHook', by_epoch=False)])
-dist_params = dict(backend='nccl')
-log_level = 'INFO'
-load_from = None
-resume_from = None
-workflow = [('train', 1)]
-cudnn_benchmark = True
-optimizer = dict(
-    type='AdamW',
-    lr=6e-05,
-    betas=(0.9, 0.999),
-    weight_decay=0.01,
-    paramwise_cfg=dict(
-        custom_keys=dict(
-            absolute_pos_embed=dict(decay_mult=0.0),
-            relative_position_bias_table=dict(decay_mult=0.0),
-            norm=dict(decay_mult=0.0))))
-optimizer_config = dict()
-lr_config = dict(
-    policy='poly',
-    warmup='linear',
-    warmup_iters=1500,
-    warmup_ratio=1e-06,
-    power=1.0,
-    min_lr=0.0,
-    by_epoch=False)
-runner = dict(type='IterBasedRunner', max_iters=80000)
-checkpoint_config = dict(by_epoch=False, interval=8000)
-evaluation = dict(interval=8000, metric='mIoU')
-work_dir = './work_dirs/upernet_swin_small_patch4_window7_512x1024_80k'
-gpu_ids = range(0, 1)